An agent-based model for consumer-to-business electronic commerce
Introduction
The explosion of Internet and the ensuing applications in electronic commerce (e-commerce) have permanently changed the outlook of traditional business trading behavior. Different business parties are now made easy to interact through Internet with others to conduct transactions in a more efficient way. Based on the nature of transactions, e-commerce is classified into following types (Turban, Lee, King, & Chung, 1999): business-to-business (B2B), business-to-consumer (B2C), consumer-to-consumer (C2C), consumer-to-business (C2B), non-business e-commerce, and intra-business e-commerce.
Compared to the three frequently mentioned models: B2B, B2C, and C2C, which are now very popular, the progress of the other one (i.e., C2B) is far left behind; it is seldom seen on the Internet. A possible reason for this situation is the high transaction cost. It takes effort to unify a group of buyers’ common needs and preferences and to interact between the buyer’s party and the potential venders in order to complete a transaction. Moreover, it is not clear how to do it; there is little research into this problem. For example, how to synthesize individual’s needs into a group’s consensus? What is the mechanism to communicate with each other within the group? How does the group collectively negotiate with a seller? All these problems need a solution if one wants to create a successful C2B trading model.
Collective purchasing is not new to the traditional business. Friends sometimes invite each other to go to a restaurant for a meal and share the expense. People join a tour to share the expense of transportation, hotels and other expenditure. In these cases, people scarify some of their personal preferences in order to gain benefits from the collectively purchasing behavior. Likewise, can we transfer such consumer behavior to the e-market? If with a suitable model and mechanism, we believe Internet will be an enabler, not an obstacle, to collective purchasing behavior because people there get easier to setup a group with common interests.
In this paper, we define and propose a model for buyer collective purchasing (BCP) behavior, which consists of a number of steps, each for a specific task, e.g., product description, buyer invitation, needs synthesis, negotiation, etc. A multi-agent architecture is devised to facilitate this job. In the framework, different agents, each assigned with a specific role, cooperate together to support the process. For example, there is an agent for each buyer who participates in collective purchasing to record the buyer’s needs and preferences. Similarly, there are agents for sellers to represent their offers to the purchase. An agent is responsible for collecting and synthesizing the buyers’ needs. Another plays the role for negotiation. Among these agents, the platform itself supports communication and interaction within the group.
Behind the multi-agent framework, there need algorithms to collect and synthesize the buyers’ preferences and to negotiate with sellers. An AHP (Analytic Hierarchy Process) algorithm is devised to synthesize the common needs from the buyer group. Based on the created AHP tree, a one-to-many negotiation algorithm takes place to seek for the best deal from potential sellers that carry products satisfying the group’s needs. Based on the proposed BCP model, we implement a prototype system to demonstrate the idea, which uses a laptop computer purchasing case to show how the model works.
The remainder of this paper is organized as follows. Section 2 discusses C2B buying behavior and defines the buyer collective purchasing model. Section 3 implements the model with a multi-agent framework. Preferences synthesis and alternatives ranking are described in Section 4, in which the algorithm of AHP used in this paper is illustrated. Section 5 describes a one-to-many negotiation algorithm that can bargain with several potential sellers simultaneously based on the information given by the constructed AHP-tree. The prototype implementation and demonstration that realize the whole idea are presented in Section 6. Finally, we conclude the paper with future research issues in Section 7.
Section snippets
C2B business model
Collective purchasing is sometimes referred to as a buyer coalition formation model (Shehory & Kraus, 1998), in which multiple buyers cooperate together to get a better offer for a specific product (or service). In this model, buyers usually specify multiple items and their valuation on them, and a group leader is elected to divide the group into coalitions and calculates the surplus division among the buyers (Yamamoto & Sycara, 2001). In particular, Tsvetovat and Sycara (2000) divide the buyer
The multi-agent system model
To automate most of the time-consuming stages of the buying process, software agent technologies have been proposed and employed in different transaction stages of e-commerce, e.g., (He et al., 2003, Liang and Huang, 2000, Maes et al., 1999). Particularly prominent among these are Guttman et al.’s review of agents in B2C e-commerce (Guttman, Moukas, & Maes, 1998a) and Sierra and Dignum’s roadmap of agent-mediated e-commerce in Europe (Sierra & Dignum, 2001). Other related sources include Liu
Preferences synthesis and alternatives ranking
This section describes the principle of the AHP method and shows how it is extended to synthesize the preferences of the buying group.
Negotiation in collective purchasing
In practice, when trading with a large quantity of products (such as the group buying case addressed here), there usually exists some negotiation space from a seller. This is particular important in C2B commerce because people here sacrifice some of their preferences with a purpose of pursuing a better deal from a supplier. However, whether the deal is good or not depends on how one “negotiates” it. Thus negotiation is a key step which determines whether a group purchasing is “worthwhile.”
Two
The multi-agent platform
We have described a multi-agent framework in which different agents behave collaboratively to support the BCP model and also explained the algorithms that work behind. Now it is the time to put them all together to show how they are implemented. Our multi-agent system platform is built on top of two major components: the Agent-Base and the Agent Communication Channel (ACC). Fig. 7 shows the conceptual architecture of this platform.
Agent-Base serves as a base class in the software system where
Conclusions and future work
Collective purchasing is a well-known consumer behavior in traditional business but is new to electronic commerce market. Also, it has not received much attention yet in the Internet. One of the many factors for this phenomenon is due to its high transaction cost; there is lack of convenient tools and environment support to facilitate the collective purchasing behavior. Furthermore, there is little academic research on its business model to realize it.
In this paper, we try to solve these
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